Overview

Dataset statistics

Number of variables19
Number of observations30998
Missing cells28582
Missing cells (%)4.9%
Duplicate rows908
Duplicate rows (%)2.9%
Total size in memory4.5 MiB
Average record size in memory152.0 B

Variable types

Categorical2
Unsupported1
Numeric16

Alerts

Dataset has 908 (2.9%) duplicate rowsDuplicates
iso_code has a high cardinality: 64 distinct values High cardinality
location has a high cardinality: 64 distinct values High cardinality
total_cases is highly correlated with iso_code and 3 other fieldsHigh correlation
new_cases is highly correlated with iso_code and 3 other fieldsHigh correlation
total_cases_per_million is highly correlated with iso_code and 4 other fieldsHigh correlation
new_cases_per_million is highly correlated with total_cases and 2 other fieldsHigh correlation
median_age is highly correlated with iso_code and 11 other fieldsHigh correlation
aged_65_older is highly correlated with iso_code and 13 other fieldsHigh correlation
aged_70_older is highly correlated with iso_code and 12 other fieldsHigh correlation
gdp_per_capita is highly correlated with iso_code and 10 other fieldsHigh correlation
cardiovasc_death_rate is highly correlated with iso_code and 9 other fieldsHigh correlation
hospital_beds_per_thousand is highly correlated with iso_code and 9 other fieldsHigh correlation
life_expectancy is highly correlated with iso_code and 9 other fieldsHigh correlation
human_development_index is highly correlated with iso_code and 11 other fieldsHigh correlation
population is highly correlated with iso_code and 5 other fieldsHigh correlation
location is highly correlated with iso_code and 15 other fieldsHigh correlation
iso_code is highly correlated with location and 15 other fieldsHigh correlation
stringency_index is highly correlated with iso_code and 5 other fieldsHigh correlation
population_density is highly correlated with iso_code and 4 other fieldsHigh correlation
diabetes_prevalence is highly correlated with iso_code and 9 other fieldsHigh correlation
stringency_index has 4320 (13.9%) missing values Missing
population_density has 2470 (8.0%) missing values Missing
median_age has 1970 (6.4%) missing values Missing
aged_65_older has 2981 (9.6%) missing values Missing
aged_70_older has 2462 (7.9%) missing values Missing
gdp_per_capita has 2490 (8.0%) missing values Missing
cardiovasc_death_rate has 1970 (6.4%) missing values Missing
diabetes_prevalence has 2008 (6.5%) missing values Missing
hospital_beds_per_thousand has 4890 (15.8%) missing values Missing
life_expectancy has 505 (1.6%) missing values Missing
human_development_index has 2514 (8.1%) missing values Missing
new_cases_per_million is highly skewed (γ1 = 23.30413895) Skewed
date is an unsupported type, check if it needs cleaning or further analysis Unsupported
new_cases has 6939 (22.4%) zeros Zeros
new_cases_per_million has 6939 (22.4%) zeros Zeros

Reproduction

Analysis started2022-11-03 18:40:41.893979
Analysis finished2022-11-03 18:41:52.574298
Duration1 minute and 10.68 seconds
Software versionpandas-profiling v3.3.0
Download configurationconfig.json

Variables

iso_code
Categorical

HIGH CARDINALITY
HIGH CORRELATION

Distinct64
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size242.3 KiB
OWID_WRL
 
536
VNM
 
535
THA
 
535
USA
 
535
TWN
 
535
Other values (59)
28322 

Length

Max length8
Median length3
Mean length3.167914059
Min length3

Characters and Unicode

Total characters98199
Distinct characters26
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPAK
2nd rowPAK
3rd rowPAK
4th rowPAK
5th rowPAK

Common Values

ValueCountFrequency (%)
OWID_WRL536
 
1.7%
VNM535
 
1.7%
THA535
 
1.7%
USA535
 
1.7%
TWN535
 
1.7%
KOR535
 
1.7%
SGP535
 
1.7%
LKA531
 
1.7%
ARE529
 
1.7%
PHL528
 
1.7%
Other values (54)25664
82.8%

Length

2022-11-04T00:11:52.751729image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
owid_wrl536
 
1.7%
twn535
 
1.7%
sgp535
 
1.7%
kor535
 
1.7%
vnm535
 
1.7%
usa535
 
1.7%
tha535
 
1.7%
lka531
 
1.7%
are529
 
1.7%
phl528
 
1.7%
Other values (54)25664
82.8%

Most occurring characters

ValueCountFrequency (%)
S11861
 
12.1%
R8535
 
8.7%
A8004
 
8.2%
T7579
 
7.7%
P5983
 
6.1%
U5706
 
5.8%
E5450
 
5.5%
N4960
 
5.1%
O4029
 
4.1%
W3835
 
3.9%
Other values (16)32257
32.8%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter97158
98.9%
Connector Punctuation1041
 
1.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
S11861
 
12.2%
R8535
 
8.8%
A8004
 
8.2%
T7579
 
7.8%
P5983
 
6.2%
U5706
 
5.9%
E5450
 
5.6%
N4960
 
5.1%
O4029
 
4.1%
W3835
 
3.9%
Other values (15)31216
32.1%
Connector Punctuation
ValueCountFrequency (%)
_1041
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin97158
98.9%
Common1041
 
1.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
S11861
 
12.2%
R8535
 
8.8%
A8004
 
8.2%
T7579
 
7.8%
P5983
 
6.2%
U5706
 
5.9%
E5450
 
5.6%
N4960
 
5.1%
O4029
 
4.1%
W3835
 
3.9%
Other values (15)31216
32.1%
Common
ValueCountFrequency (%)
_1041
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII98199
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
S11861
 
12.1%
R8535
 
8.7%
A8004
 
8.2%
T7579
 
7.7%
P5983
 
6.1%
U5706
 
5.8%
E5450
 
5.5%
N4960
 
5.1%
O4029
 
4.1%
W3835
 
3.9%
Other values (16)32257
32.8%

location
Categorical

HIGH CARDINALITY
HIGH CORRELATION

Distinct64
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size242.3 KiB
World
 
536
Vietnam
 
535
Thailand
 
535
United States
 
535
Taiwan
 
535
Other values (59)
28322 

Length

Max length32
Median length19
Mean length9.275146784
Min length4

Characters and Unicode

Total characters287511
Distinct characters43
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPakistan
2nd rowPakistan
3rd rowPakistan
4th rowPakistan
5th rowPakistan

Common Values

ValueCountFrequency (%)
World536
 
1.7%
Vietnam535
 
1.7%
Thailand535
 
1.7%
United States535
 
1.7%
Taiwan535
 
1.7%
South Korea535
 
1.7%
Singapore535
 
1.7%
Sri Lanka531
 
1.7%
United Arab Emirates529
 
1.7%
Philippines528
 
1.7%
Other values (54)25664
82.8%

Length

2022-11-04T00:11:52.945247image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
south1995
 
4.5%
and1902
 
4.3%
united1591
 
3.6%
saint1441
 
3.2%
sudan946
 
2.1%
world536
 
1.2%
taiwan535
 
1.2%
korea535
 
1.2%
thailand535
 
1.2%
states535
 
1.2%
Other values (71)33974
76.3%

Most occurring characters

ValueCountFrequency (%)
a42169
14.7%
i27284
 
9.5%
n25991
 
9.0%
e22234
 
7.7%
S13867
 
4.8%
13527
 
4.7%
r13424
 
4.7%
t13221
 
4.6%
o12454
 
4.3%
d10740
 
3.7%
Other values (33)92600
32.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter231845
80.6%
Uppercase Letter42139
 
14.7%
Space Separator13527
 
4.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a42169
18.2%
i27284
11.8%
n25991
11.2%
e22234
9.6%
r13424
 
5.8%
t13221
 
5.7%
o12454
 
5.4%
d10740
 
4.6%
u10286
 
4.4%
l7543
 
3.3%
Other values (14)46499
20.1%
Uppercase Letter
ValueCountFrequency (%)
S13867
32.9%
T5366
 
12.7%
P4922
 
11.7%
U3531
 
8.4%
V2238
 
5.3%
A2023
 
4.8%
K1535
 
3.6%
R1512
 
3.6%
L1482
 
3.5%
G962
 
2.3%
Other values (8)4701
 
11.2%
Space Separator
ValueCountFrequency (%)
13527
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin273984
95.3%
Common13527
 
4.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
a42169
15.4%
i27284
 
10.0%
n25991
 
9.5%
e22234
 
8.1%
S13867
 
5.1%
r13424
 
4.9%
t13221
 
4.8%
o12454
 
4.5%
d10740
 
3.9%
u10286
 
3.8%
Other values (32)82314
30.0%
Common
ValueCountFrequency (%)
13527
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII287511
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a42169
14.7%
i27284
 
9.5%
n25991
 
9.0%
e22234
 
7.7%
S13867
 
4.8%
13527
 
4.7%
r13424
 
4.7%
t13221
 
4.6%
o12454
 
4.3%
d10740
 
3.7%
Other values (33)92600
32.2%

date
Unsupported

REJECTED
UNSUPPORTED

Missing0
Missing (%)0.0%
Memory size242.3 KiB

total_cases
Real number (ℝ≥0)

HIGH CORRELATION

Distinct19889
Distinct (%)64.2%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean1769545.217
Minimum1
Maximum186000000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size242.3 KiB
2022-11-04T00:11:53.162623image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile11
Q1943
median13569
Q3220800
95-th percentile3835023.4
Maximum186000000
Range185999999
Interquartile range (IQR)219857

Descriptive statistics

Standard deviation11834911.17
Coefficient of variation (CV)6.688108932
Kurtosis136.7622935
Mean1769545.217
Median Absolute Deviation (MAD)13556
Skewness11.06952215
Sum5.485059308 × 1010
Variance1.400651224 × 1014
MonotonicityNot monotonic
2022-11-04T00:11:53.404206image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
509435
 
1.4%
1401
 
1.3%
27348
 
1.1%
3278
 
0.9%
2211
 
0.7%
12196
 
0.6%
17185
 
0.6%
13308164
 
0.5%
4160
 
0.5%
11139
 
0.4%
Other values (19879)28480
91.9%
ValueCountFrequency (%)
1401
1.3%
2211
0.7%
3278
0.9%
4160
 
0.5%
545
 
0.1%
638
 
0.1%
754
 
0.2%
8131
 
0.4%
939
 
0.1%
1084
 
0.3%
ValueCountFrequency (%)
1860000003
< 0.1%
1850000002
< 0.1%
1840000002
< 0.1%
1830000003
< 0.1%
1820000002
< 0.1%
1810000003
< 0.1%
1800000003
< 0.1%
1790000002
< 0.1%
1780000003
< 0.1%
1770000003
< 0.1%

new_cases
Real number (ℝ)

HIGH CORRELATION
ZEROS

Distinct6766
Distinct (%)21.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9532.992741
Minimum-74347
Maximum906017
Zeros6939
Zeros (%)22.4%
Negative20
Negative (%)0.1%
Memory size242.3 KiB
2022-11-04T00:11:53.651792image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum-74347
5-th percentile0
Q11
median75
Q31075
95-th percentile23634.55
Maximum906017
Range980364
Interquartile range (IQR)1074

Descriptive statistics

Standard deviation57106.71986
Coefficient of variation (CV)5.990429387
Kurtosis103.3907676
Mean9532.992741
Median Absolute Deviation (MAD)75
Skewness9.562198231
Sum295503709
Variance3261177454
MonotonicityNot monotonic
2022-11-04T00:11:53.884080image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
06939
 
22.4%
1862
 
2.8%
2536
 
1.7%
3406
 
1.3%
4372
 
1.2%
5337
 
1.1%
6301
 
1.0%
7291
 
0.9%
8232
 
0.7%
9217
 
0.7%
Other values (6756)20505
66.1%
ValueCountFrequency (%)
-743471
< 0.1%
-100341
< 0.1%
-47871
< 0.1%
-23621
< 0.1%
-3721
< 0.1%
-1611
< 0.1%
-1041
< 0.1%
-211
< 0.1%
-191
< 0.1%
-161
< 0.1%
ValueCountFrequency (%)
9060171
< 0.1%
9037591
< 0.1%
8997761
< 0.1%
8976541
< 0.1%
8899731
< 0.1%
8808841
< 0.1%
8790111
< 0.1%
8700151
< 0.1%
8549351
< 0.1%
8541011
< 0.1%

total_cases_per_million
Real number (ℝ≥0)

HIGH CORRELATION

Distinct23890
Distinct (%)77.1%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean13914.38625
Minimum0.003
Maximum169605.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size242.3 KiB
2022-11-04T00:11:54.119229image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0.003
5-th percentile3.256
Q1158.044
median1701.452
Q314745.14
95-th percentile72842.272
Maximum169605.5
Range169605.497
Interquartile range (IQR)14587.096

Descriptive statistics

Standard deviation25091.04914
Coefficient of variation (CV)1.803245123
Kurtosis6.369535168
Mean13914.38625
Median Absolute Deviation (MAD)1692.931
Skewness2.453856398
Sum431304230.5
Variance629560746.9
MonotonicityNot monotonic
2022-11-04T00:11:54.352516image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8.521429
 
1.4%
33374.54267
 
0.9%
1395.313164
 
0.5%
14833.13159
 
0.5%
15.12146
 
0.5%
3.256118
 
0.4%
18.203102
 
0.3%
29.11786
 
0.3%
111.85782
 
0.3%
10.0881
 
0.3%
Other values (23880)29363
94.7%
ValueCountFrequency (%)
0.0031
 
< 0.1%
0.0053
 
< 0.1%
0.0062
 
< 0.1%
0.0073
 
< 0.1%
0.0097
 
< 0.1%
0.0122
 
< 0.1%
0.01431
0.1%
0.0153
 
< 0.1%
0.0175
 
< 0.1%
0.01810
 
< 0.1%
ValueCountFrequency (%)
169605.53
 
< 0.1%
165792.23
 
< 0.1%
161246.73
 
< 0.1%
158419.84
 
< 0.1%
153711.63
 
< 0.1%
150038.36
 
< 0.1%
150008.87
 
< 0.1%
149979.428
0.1%
149949.97
 
< 0.1%
1498917
 
< 0.1%

new_cases_per_million
Real number (ℝ)

HIGH CORRELATION
SKEWED
ZEROS

Distinct15200
Distinct (%)49.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean78.7413959
Minimum-1590.15
Maximum18293.68
Zeros6939
Zeros (%)22.4%
Negative20
Negative (%)0.1%
Memory size242.3 KiB
2022-11-04T00:11:54.609740image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum-1590.15
5-th percentile0
Q10.084
median7.1385
Q365.349
95-th percentile378.01495
Maximum18293.68
Range19883.83
Interquartile range (IQR)65.265

Descriptive statistics

Standard deviation254.9380578
Coefficient of variation (CV)3.237662412
Kurtosis1155.265588
Mean78.7413959
Median Absolute Deviation (MAD)7.1385
Skewness23.30413895
Sum2440825.79
Variance64993.41329
MonotonicityNot monotonic
2022-11-04T00:11:54.851426image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
06939
 
22.4%
0.04292
 
0.3%
4.56373
 
0.2%
0.0164
 
0.2%
0.08460
 
0.2%
0.06753
 
0.2%
0.16847
 
0.2%
0.03447
 
0.2%
0.01446
 
0.1%
0.07244
 
0.1%
Other values (15190)23533
75.9%
ValueCountFrequency (%)
-1590.151
< 0.1%
-559.8441
< 0.1%
-265.1891
< 0.1%
-214.6091
< 0.1%
-70.5151
< 0.1%
-34.7941
< 0.1%
-15.7891
< 0.1%
-7.9561
< 0.1%
-6.0451
< 0.1%
-2.2741
< 0.1%
ValueCountFrequency (%)
18293.681
< 0.1%
10290.831
< 0.1%
9924.7511
< 0.1%
8652.6582
< 0.1%
6680.9031
< 0.1%
5897.9051
< 0.1%
5460.6471
< 0.1%
5155.5831
< 0.1%
5084.4011
< 0.1%
5053.8951
< 0.1%

stringency_index
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct162
Distinct (%)0.6%
Missing4320
Missing (%)13.9%
Infinite0
Infinite (%)0.0%
Mean57.32864083
Minimum0
Maximum100
Zeros85
Zeros (%)0.3%
Negative0
Negative (%)0.0%
Memory size242.3 KiB
2022-11-04T00:11:55.112686image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile23.15
Q143.52
median58.8
Q372.69
95-th percentile87.96
Maximum100
Range100
Interquartile range (IQR)29.17

Descriptive statistics

Standard deviation20.34080889
Coefficient of variation (CV)0.3548105902
Kurtosis-0.4478641943
Mean57.32864083
Median Absolute Deviation (MAD)14.35
Skewness-0.3351492706
Sum1529413.48
Variance413.7485063
MonotonicityNot monotonic
2022-11-04T00:11:55.349367image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
69.44550
 
1.8%
64.81531
 
1.7%
50531
 
1.7%
50.93518
 
1.7%
71.3508
 
1.6%
60.19473
 
1.5%
23.15456
 
1.5%
63.89453
 
1.5%
47.22441
 
1.4%
31.48431
 
1.4%
Other values (152)21786
70.3%
(Missing)4320
 
13.9%
ValueCountFrequency (%)
085
0.3%
2.7866
0.2%
5.5667
0.2%
6.4850
 
0.2%
8.33133
0.4%
11.11145
0.5%
12.0449
 
0.2%
12.963
 
< 0.1%
13.8994
0.3%
14.8115
 
< 0.1%
ValueCountFrequency (%)
10097
0.3%
97.228
 
< 0.1%
96.3172
0.6%
94.4494
0.3%
93.52127
0.4%
92.5943
 
0.1%
91.6750
 
0.2%
90.74176
0.6%
89.81158
0.5%
88.8984
0.3%

population
Real number (ℝ≥0)

HIGH CORRELATION

Distinct64
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean174331044.5
Minimum809
Maximum7790000000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size242.3 KiB
2022-11-04T00:11:55.921610image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum809
5-th percentile98340
Q15101416
median14862927
Q343849269
95-th percentile331000000
Maximum7790000000
Range7789999191
Interquartile range (IQR)38747853

Descriptive statistics

Standard deviation1012979445
Coefficient of variation (CV)5.810665837
Kurtosis52.25370075
Mean174331044.5
Median Absolute Deviation (MAD)14176049
Skewness7.345455437
Sum5.403913717 × 1012
Variance1.026127355 × 1018
MonotonicityNot monotonic
2022-11-04T00:11:56.421759image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7790000000536
 
1.7%
97338583535
 
1.7%
69799978535
 
1.7%
331000000535
 
1.7%
23816775535
 
1.7%
51269183535
 
1.7%
5850343535
 
1.7%
21413250531
 
1.7%
9890400529
 
1.7%
110000000528
 
1.7%
Other values (54)25664
82.8%
ValueCountFrequency (%)
809492
1.6%
33938500
1.6%
53192473
1.5%
98340484
1.6%
110947484
1.6%
183629484
1.6%
198410235
0.8%
219161461
1.5%
307150243
0.8%
586634484
1.6%
ValueCountFrequency (%)
7790000000536
1.7%
431000000505
1.6%
331000000535
1.7%
221000000502
1.6%
146000000527
1.7%
110000000528
1.7%
97338583535
1.7%
84339067486
1.6%
69799978535
1.7%
67886004527
1.7%

population_density
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct59
Distinct (%)0.2%
Missing2470
Missing (%)8.0%
Infinite0
Infinite (%)0.0%
Mean296.303544
Minimum3.612
Maximum7915.731
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size242.3 KiB
2022-11-04T00:11:56.691848image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum3.612
5-th percentile15.322
Q142.729
median93.105
Q3214.243
95-th percentile556.667
Maximum7915.731
Range7912.119
Interquartile range (IQR)171.514

Descriptive statistics

Standard deviation1064.769658
Coefficient of variation (CV)3.593509694
Kurtosis46.18993025
Mean296.303544
Median Absolute Deviation (MAD)69.605
Skewness6.867257111
Sum8452947.503
Variance1133734.424
MonotonicityNot monotonic
2022-11-04T00:11:56.977937image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
58.045536
 
1.7%
7915.731535
 
1.7%
308.127535
 
1.7%
35.608535
 
1.7%
135.132535
 
1.7%
527.967535
 
1.7%
341.955531
 
1.7%
112.442529
 
1.7%
351.873528
 
1.7%
272.898527
 
1.7%
Other values (49)23202
74.8%
(Missing)2470
 
8.0%
ValueCountFrequency (%)
3.612484
1.6%
8.823527
1.7%
15.322496
1.6%
17.144490
1.6%
18.22478
1.5%
19.751485
1.6%
21.841272
0.9%
22.662243
0.8%
22.995480
1.5%
23.258484
1.6%
ValueCountFrequency (%)
7915.731535
1.7%
778.202493
1.6%
556.667500
1.6%
527.967535
1.7%
494.869484
1.6%
351.873528
1.7%
341.955531
1.7%
308.127535
1.7%
293.187484
1.6%
281.787484
1.6%

median_age
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct54
Distinct (%)0.2%
Missing1970
Missing (%)6.4%
Infinite0
Infinite (%)0.0%
Mean30.68835607
Minimum16.4
Maximum46.2
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size242.3 KiB
2022-11-04T00:11:57.622190image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum16.4
5-th percentile17.7
Q120.8
median31.6
Q340.1
95-th percentile44.5
Maximum46.2
Range29.8
Interquartile range (IQR)19.3

Descriptive statistics

Standard deviation9.236321861
Coefficient of variation (CV)0.3009715424
Kurtosis-1.376671719
Mean30.68835607
Median Absolute Deviation (MAD)9.4
Skewness0.01610687223
Sum890821.6
Variance85.30964152
MonotonicityNot monotonic
2022-11-04T00:11:57.830293image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
31.9994
 
3.2%
41.2984
 
3.2%
36.2968
 
3.1%
17.7962
 
3.1%
18.7957
 
3.1%
20.3941
 
3.0%
30.9536
 
1.7%
42.4535
 
1.7%
40.1535
 
1.7%
38.3535
 
1.7%
Other values (44)21081
68.0%
(Missing)1970
 
6.4%
ValueCountFrequency (%)
16.4477
1.5%
16.8482
1.6%
17.7962
3.1%
18476
1.5%
18.7957
3.1%
19.1467
1.5%
19.2462
1.5%
19.4492
1.6%
19.6478
1.5%
19.7484
1.6%
ValueCountFrequency (%)
46.2496
1.6%
45.5526
1.7%
44.5493
1.6%
43.4535
1.7%
43.1502
1.6%
43501
1.6%
42.4535
1.7%
42.2535
1.7%
41.8494
1.6%
41.4495
1.6%

aged_65_older
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct58
Distinct (%)0.2%
Missing2981
Missing (%)9.6%
Infinite0
Infinite (%)0.0%
Mean8.59813167
Minimum1.144
Maximum21.502
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size242.3 KiB
2022-11-04T00:11:58.068867image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1.144
5-th percentile2.168
Q13.295
median7.15
Q314.178
95-th percentile19.436
Maximum21.502
Range20.358
Interquartile range (IQR)10.883

Descriptive statistics

Standard deviation5.967122673
Coefficient of variation (CV)0.694002244
Kurtosis-0.9627484729
Mean8.59813167
Median Absolute Deviation (MAD)4.142
Skewness0.6456300947
Sum240893.855
Variance35.606553
MonotonicityNot monotonic
2022-11-04T00:11:58.744065image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8.696536
 
1.7%
13.914535
 
1.7%
12.922535
 
1.7%
7.15535
 
1.7%
15.413535
 
1.7%
11.373535
 
1.7%
10.069531
 
1.7%
1.144529
 
1.7%
4.803528
 
1.7%
18.517527
 
1.7%
Other values (48)22691
73.2%
(Missing)2981
 
9.6%
ValueCountFrequency (%)
1.144529
1.7%
1.307498
1.6%
2.168477
1.5%
2.48480
1.5%
2.538467
1.5%
2.731482
1.6%
2.822478
1.5%
2.839492
1.6%
2.886461
1.5%
2.922457
1.5%
ValueCountFrequency (%)
21.502496
1.6%
19.985526
1.7%
19.436526
1.7%
19.062493
1.6%
18.517527
1.7%
18.436502
1.6%
17.85501
1.6%
17.366492
1.6%
16.763494
1.6%
16.462495
1.6%

aged_70_older
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct59
Distinct (%)0.2%
Missing2462
Missing (%)7.9%
Infinite0
Infinite (%)0.0%
Mean5.363135548
Minimum0.526
Maximum14.924
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size242.3 KiB
2022-11-04T00:11:59.036503image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0.526
5-th percentile1.285
Q11.897
median4.229
Q38.42025
95-th percentile13.433
Maximum14.924
Range14.398
Interquartile range (IQR)6.52325

Descriptive statistics

Standard deviation4.01341372
Coefficient of variation (CV)0.7483334485
Kurtosis-0.553274358
Mean5.363135548
Median Absolute Deviation (MAD)2.384
Skewness0.8284391251
Sum153042.436
Variance16.10748968
MonotonicityNot monotonic
2022-11-04T00:11:59.276935image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5.355536
 
1.7%
7.049535
 
1.7%
4.718535
 
1.7%
9.732535
 
1.7%
6.89535
 
1.7%
8.353535
 
1.7%
8.622535
 
1.7%
5.331531
 
1.7%
0.526529
 
1.7%
2.661528
 
1.7%
Other values (49)23202
74.8%
(Missing)2462
 
7.9%
ValueCountFrequency (%)
0.526529
1.7%
0.617498
1.6%
1.285467
1.5%
1.308477
1.5%
1.496482
1.6%
1.525492
1.6%
1.542480
1.5%
1.583457
1.5%
1.642484
1.6%
1.726493
1.6%
ValueCountFrequency (%)
14.924496
1.6%
13.799526
1.7%
13.433526
1.7%
12.93493
1.6%
12.644502
1.6%
12.527527
1.7%
11.69501
1.6%
11.133495
1.6%
10.361485
1.6%
10.202494
1.6%

gdp_per_capita
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct59
Distinct (%)0.2%
Missing2490
Missing (%)8.0%
Infinite0
Infinite (%)0.0%
Mean21259.66612
Minimum1390.3
Maximum116935.6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size242.3 KiB
2022-11-04T00:11:59.517923image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1390.3
5-th percentile1569.888
Q14449.898
median12951.84
Q328763.07
95-th percentile67293.48
Maximum116935.6
Range115545.3
Interquartile range (IQR)24313.172

Descriptive statistics

Standard deviation22849.76906
Coefficient of variation (CV)1.074794352
Kurtosis4.375845134
Mean21259.66612
Median Absolute Deviation (MAD)10361.36
Skewness1.934011606
Sum606070561.7
Variance522111946.2
MonotonicityNot monotonic
2022-11-04T00:11:59.768527image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
15469.21536
 
1.7%
35938.37535
 
1.7%
85535.38535
 
1.7%
6171.884535
 
1.7%
54225.45535
 
1.7%
16277.67535
 
1.7%
11669.08531
 
1.7%
67293.48529
 
1.7%
7599.188528
 
1.7%
39753.24527
 
1.7%
Other values (49)23182
74.8%
(Missing)2490
 
8.0%
ValueCountFrequency (%)
1390.3467
1.5%
1429.813492
1.6%
1479.147457
1.5%
1569.888462
1.5%
1697.707477
1.5%
1854.211484
1.6%
1899.775478
1.5%
2205.923272
0.9%
2470.58496
1.6%
2683.304482
1.6%
ValueCountFrequency (%)
116935.6498
1.6%
85535.38535
1.7%
67293.48529
1.7%
57410.17502
1.6%
56861.47500
1.6%
54225.45535
1.7%
49045.41496
1.6%
46949.28526
1.7%
39753.24527
1.7%
35938.37535
1.7%

cardiovasc_death_rate
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct60
Distinct (%)0.2%
Missing1970
Missing (%)6.4%
Infinite0
Infinite (%)0.0%
Mean265.7203056
Minimum85.755
Maximum724.417
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size242.3 KiB
2022-11-04T00:12:00.860245image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum85.755
5-th percentile92.243
Q1171.285
median241.219
Q3335.346
95-th percentile539.849
Maximum724.417
Range638.662
Interquartile range (IQR)164.061

Descriptive statistics

Standard deviation134.0078079
Coefficient of variation (CV)0.5043190344
Kurtosis0.9762589741
Mean265.7203056
Median Absolute Deviation (MAD)84.502
Skewness0.9865088341
Sum7713329.032
Variance17958.09259
MonotonicityNot monotonic
2022-11-04T00:12:01.142922image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
233.07536
 
1.7%
109.861535
 
1.7%
151.089535
 
1.7%
103.957535
 
1.7%
85.998535
 
1.7%
92.243535
 
1.7%
245.465535
 
1.7%
197.093531
 
1.7%
317.84529
 
1.7%
370.437528
 
1.7%
Other values (50)23694
76.4%
(Missing)1970
 
6.4%
ValueCountFrequency (%)
85.755492
1.6%
85.998535
1.7%
92.243535
1.7%
99.403526
1.7%
99.739502
1.6%
103.957535
1.7%
109.861535
1.7%
122.137527
1.7%
127.842496
1.6%
128.346488
1.6%
ValueCountFrequency (%)
724.417483
1.6%
561.494478
1.5%
546.3243
0.8%
539.849495
1.6%
495.003457
1.5%
459.78272
0.9%
439.415492
1.6%
431.388484
1.6%
431.297527
1.7%
427.698437
1.4%

diabetes_prevalence
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct55
Distinct (%)0.2%
Missing2008
Missing (%)6.5%
Infinite0
Infinite (%)0.0%
Mean8.395268713
Minimum1.82
Maximum18.68
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size242.3 KiB
2022-11-04T00:12:01.411372image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1.82
5-th percentile2.42
Q15.91
median7.25
Q310.68
95-th percentile17.26
Maximum18.68
Range16.86
Interquartile range (IQR)4.77

Descriptive statistics

Standard deviation3.905789579
Coefficient of variation (CV)0.4652369938
Kurtosis0.2470358436
Mean8.395268713
Median Absolute Deviation (MAD)2.46
Skewness0.7578577997
Sum243378.84
Variance15.25519223
MonotonicityNot monotonic
2022-11-04T00:12:01.645216image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.421424
 
4.6%
4.281011
 
3.3%
11.62968
 
3.1%
7.11932
 
3.0%
8.51536
 
1.7%
10.99535
 
1.7%
6.8535
 
1.7%
7.04535
 
1.7%
10.79535
 
1.7%
6535
 
1.7%
Other values (45)21444
69.2%
(Missing)2008
 
6.5%
ValueCountFrequency (%)
1.82478
 
1.5%
2.421424
4.6%
2.5477
 
1.5%
3.94480
 
1.5%
4.281011
3.3%
4.79526
 
1.7%
5.35457
 
1.5%
5.52493
 
1.6%
5.59502
 
1.6%
5.64500
 
1.6%
ValueCountFrequency (%)
18.68272
0.9%
17.72496
1.6%
17.65478
1.5%
17.26529
1.7%
16.52498
1.6%
15.67484
1.6%
12.84473
1.5%
12.54484
1.6%
12.13486
1.6%
12.02243
0.8%

hospital_beds_per_thousand
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct44
Distinct (%)0.2%
Missing4890
Missing (%)15.8%
Infinite0
Infinite (%)0.0%
Mean3.079726904
Minimum0.5
Maximum12.27
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size242.3 KiB
2022-11-04T00:12:01.894192image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0.5
5-th percentile0.7
Q11.4
median2.6
Q33.6
95-th percentile8.05
Maximum12.27
Range11.77
Interquartile range (IQR)2.2

Descriptive statistics

Standard deviation2.324190019
Coefficient of variation (CV)0.7546740641
Kurtosis3.702723096
Mean3.079726904
Median Absolute Deviation (MAD)1.2
Skewness1.768118245
Sum80405.51
Variance5.401859243
MonotonicityNot monotonic
2022-11-04T00:12:02.148051image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=44)
ValueCountFrequency (%)
2.31455
 
4.7%
0.71431
 
4.6%
1.21027
 
3.3%
2.61019
 
3.3%
3.61015
 
3.3%
1.3974
 
3.1%
0.8968
 
3.1%
2.705536
 
1.7%
12.27535
 
1.7%
2.1535
 
1.7%
Other values (34)16613
53.6%
(Missing)4890
 
15.8%
ValueCountFrequency (%)
0.5477
 
1.5%
0.6502
 
1.6%
0.71431
4.6%
0.8968
3.1%
0.9482
 
1.6%
1528
 
1.7%
1.21027
3.3%
1.3974
3.1%
1.4272
 
0.9%
1.5476
 
1.5%
ValueCountFrequency (%)
12.27535
1.7%
8.8495
1.6%
8.05527
1.7%
6.892501
1.6%
6.62494
1.6%
5.9476
1.5%
5.82492
1.6%
5.609492
1.6%
4.8437
1.4%
4.53502
1.6%

life_expectancy
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct60
Distinct (%)0.2%
Missing505
Missing (%)1.6%
Infinite0
Infinite (%)0.0%
Mean73.42964713
Minimum54.7
Maximum84.97
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size242.3 KiB
2022-11-04T00:12:02.380210image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum54.7
5-th percentile61.04
Q169.5
median74.05
Q377.97
95-th percentile83.62
Maximum84.97
Range30.27
Interquartile range (IQR)8.47

Descriptive statistics

Standard deviation6.94559675
Coefficient of variation (CV)0.09458845331
Kurtosis-0.09323358249
Mean73.42964713
Median Absolute Deviation (MAD)3.92
Skewness-0.6022047581
Sum2239090.23
Variance48.24131421
MonotonicityNot monotonic
2022-11-04T00:12:02.614940image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
72.581063
 
3.4%
81.321020
 
3.3%
72.06979
 
3.2%
83.62535
 
1.7%
75.4535
 
1.7%
78.86535
 
1.7%
77.15535
 
1.7%
80.46535
 
1.7%
83.03535
 
1.7%
76.98531
 
1.7%
Other values (50)23690
76.4%
ValueCountFrequency (%)
54.7467
1.5%
57.4482
1.6%
57.85462
1.5%
61.04492
1.6%
61.49478
1.5%
63.37477
1.5%
63.89480
1.5%
64.13493
1.6%
64.5478
1.5%
65.31484
1.6%
ValueCountFrequency (%)
84.97500
1.6%
83.78502
1.6%
83.62535
1.7%
83.56526
1.7%
83.03535
1.7%
82.8526
1.7%
82.05496
1.6%
81.321020
3.3%
80.46535
1.7%
80.23498
1.6%

human_development_index
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct54
Distinct (%)0.2%
Missing2514
Missing (%)8.1%
Infinite0
Infinite (%)0.0%
Mean0.7327258461
Minimum0.433
Maximum0.955
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size242.3 KiB
2022-11-04T00:12:02.860892image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0.433
5-th percentile0.51
Q10.606
median0.74
Q30.848
95-th percentile0.938
Maximum0.955
Range0.522
Interquartile range (IQR)0.242

Descriptive statistics

Standard deviation0.1424659474
Coefficient of variation (CV)0.1944328129
Kurtosis-0.9252927506
Mean0.7327258461
Median Absolute Deviation (MAD)0.115
Skewness-0.3632708611
Sum20870.963
Variance0.02029654615
MonotonicityNot monotonic
2022-11-04T00:12:03.114762image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.7771027
 
3.3%
0.796968
 
3.1%
0.738968
 
3.1%
0.779968
 
3.1%
0.567748
 
2.4%
0.737536
 
1.7%
0.916535
 
1.7%
0.938535
 
1.7%
0.926535
 
1.7%
0.704535
 
1.7%
Other values (44)21129
68.2%
(Missing)2514
 
8.1%
ValueCountFrequency (%)
0.433462
1.5%
0.452467
1.5%
0.47457
1.5%
0.51484
1.6%
0.512496
1.6%
0.515492
1.6%
0.529482
1.6%
0.543484
1.6%
0.544477
1.5%
0.555478
1.5%
ValueCountFrequency (%)
0.955502
1.6%
0.945526
1.7%
0.938535
1.7%
0.932527
1.7%
0.926535
1.7%
0.917493
1.6%
0.916535
1.7%
0.904526
1.7%
0.89529
1.7%
0.88494
1.6%

Interactions

2022-11-04T00:11:46.711839image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-04T00:10:46.221477image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
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2022-11-04T00:10:52.902659image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
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2022-11-04T00:11:00.123384image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
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2022-11-04T00:11:06.307801image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
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2022-11-04T00:11:12.391527image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-04T00:11:15.369562image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
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2022-11-04T00:11:20.498843image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-04T00:11:27.184319image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-04T00:11:35.052494image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-04T00:11:41.122417image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-04T00:11:45.251574image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-04T00:11:49.433049image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-04T00:10:48.875422image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-04T00:10:52.027998image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-04T00:10:56.145985image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-04T00:10:59.317539image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-04T00:11:02.572839image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-04T00:11:05.591717image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-04T00:11:08.585708image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-04T00:11:11.644526image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-04T00:11:14.639515image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-04T00:11:17.541791image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-04T00:11:20.681355image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-04T00:11:27.495650image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-04T00:11:35.755544image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-04T00:11:41.309947image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-04T00:11:45.480084image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-04T00:11:49.653843image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-04T00:10:49.072852image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-04T00:10:52.195551image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-04T00:10:56.327497image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-04T00:10:59.558894image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-04T00:11:02.755344image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-04T00:11:05.772233image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-04T00:11:08.768218image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-04T00:11:11.815114image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-04T00:11:14.824019image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-04T00:11:17.727258image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-04T00:11:20.866856image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-04T00:11:28.759906image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-04T00:11:36.325343image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-04T00:11:41.522189image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-04T00:11:45.702909image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-04T00:11:49.878744image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-04T00:10:49.314204image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-04T00:10:52.529661image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-04T00:10:56.514043image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-04T00:10:59.747349image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-04T00:11:02.925887image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-04T00:11:05.945768image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-04T00:11:09.126263image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-04T00:11:12.003565image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-04T00:11:15.008526image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-04T00:11:17.895806image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-04T00:11:21.046376image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-04T00:11:29.481293image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-04T00:11:36.855099image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-04T00:11:41.718823image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-04T00:11:45.912450image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-04T00:11:50.089356image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-04T00:10:49.597446image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-04T00:10:52.736105image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-04T00:10:56.704528image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-04T00:10:59.930858image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-04T00:11:03.122323image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-04T00:11:06.128280image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-04T00:11:09.306818image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-04T00:11:12.215998image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-04T00:11:15.193034image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-04T00:11:18.068342image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-04T00:11:21.241852image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-04T00:11:30.312210image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-04T00:11:37.387327image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-04T00:11:41.914446image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-04T00:11:46.127727image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Correlations

2022-11-04T00:12:03.354767image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-11-04T00:12:03.794205image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-11-04T00:12:04.245640image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-11-04T00:12:04.642931image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2022-11-04T00:12:04.868156image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-11-04T00:11:50.521858image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
A simple visualization of nullity by column.
2022-11-04T00:11:51.153108image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2022-11-04T00:11:51.780989image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2022-11-04T00:11:52.305179image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

iso_codelocationdatetotal_casesnew_casestotal_cases_per_millionnew_cases_per_millionstringency_indexpopulationpopulation_densitymedian_ageaged_65_olderaged_70_oldergdp_per_capitacardiovasc_death_ratediabetes_prevalencehospital_beds_per_thousandlife_expectancyhuman_development_index
0PAKPakistan25-02-20202.020.0090.00919.44221000000255.57323.54.4952.785034.708423.0318.350.667.270.557
1PAKPakistan26-02-20202.000.0090.00019.44221000000255.57323.54.4952.785034.708423.0318.350.667.270.557
2PAKPakistan27-02-20202.000.0090.00028.70221000000255.57323.54.4952.785034.708423.0318.350.667.270.557
3PAKPakistan28-02-20204.020.0180.00928.70221000000255.57323.54.4952.785034.708423.0318.350.667.270.557
4PAKPakistan29-02-20204.000.0180.00028.70221000000255.57323.54.4952.785034.708423.0318.350.667.270.557
5PAKPakistan2020-01-03 00:00:004.000.0180.00028.70221000000255.57323.54.4952.785034.708423.0318.350.667.270.557
6PAKPakistan2020-02-03 00:00:005.010.0230.00528.70221000000255.57323.54.4952.785034.708423.0318.350.667.270.557
7PAKPakistan2020-03-03 00:00:005.000.0230.00028.70221000000255.57323.54.4952.785034.708423.0318.350.667.270.557
8PAKPakistan2020-04-03 00:00:005.000.0230.00028.70221000000255.57323.54.4952.785034.708423.0318.350.667.270.557
9PAKPakistan2020-05-03 00:00:006.010.0270.00528.70221000000255.57323.54.4952.785034.708423.0318.350.667.270.557

Last rows

iso_codelocationdatetotal_casesnew_casestotal_cases_per_millionnew_cases_per_millionstringency_indexpopulationpopulation_densitymedian_ageaged_65_olderaged_70_oldergdp_per_capitacardiovasc_death_ratediabetes_prevalencehospital_beds_per_thousandlife_expectancyhuman_development_index
30988ZWEZimbabwe2021-01-07 00:00:0051221.013573446.22691.301NaN1486292742.72919.62.8221.8821899.775307.8461.821.761.490.571
30989ZWEZimbabwe2021-02-07 00:00:0052663.014423543.24697.020NaN1486292742.72919.62.8221.8821899.775307.8461.821.761.490.571
30990ZWEZimbabwe2021-03-07 00:00:0053665.010023610.66267.416NaN1486292742.72919.62.8221.8821899.775307.8461.821.761.490.571
30991ZWEZimbabwe2021-04-07 00:00:0054474.08093665.09254.431NaN1486292742.72919.62.8221.8821899.775307.8461.821.761.490.571
30992ZWEZimbabwe2021-05-07 00:00:0056014.015403768.706103.614NaN1486292742.72919.62.8221.8821899.775307.8461.821.761.490.571
30993ZWEZimbabwe2021-06-07 00:00:0057963.019493899.837131.132NaN1486292742.72919.62.8221.8821899.775307.8461.821.761.490.571
30994ZWEZimbabwe2021-07-07 00:00:0060227.022644052.163152.325NaN1486292742.72919.62.8221.8821899.775307.8461.821.761.490.571
30995ZWEZimbabwe2021-08-07 00:00:0062383.021564197.222145.059NaN1486292742.72919.62.8221.8821899.775307.8461.821.761.490.571
30996ZWEZimbabwe2021-09-07 00:00:0065066.026834377.738180.516NaN1486292742.72919.62.8221.8821899.775307.8461.821.761.490.571
30997ZWEZimbabwe2021-10-07 00:00:0066853.017874497.970120.232NaN1486292742.72919.62.8221.8821899.775307.8461.821.761.490.571

Duplicate rows

Most frequently occurring

iso_codelocationtotal_casesnew_casestotal_cases_per_millionnew_cases_per_millionstringency_indexpopulationpopulation_densitymedian_ageaged_65_olderaged_70_oldergdp_per_capitacardiovasc_death_ratediabetes_prevalencehospital_beds_per_thousandlife_expectancyhuman_development_index# duplicates
789VATVatican27.0033374.5400.0NaN809NaNNaNNaNNaNNaNNaNNaNNaN75.12NaN266
787VATVatican12.0014833.1300.0NaN809NaNNaNNaNNaNNaNNaNNaNNaN75.12NaN158
866WSMSamoa3.0015.1200.0NaN19841069.41322.05.6063.5646021.557348.9779.21NaN73.320.715145
865WSMSamoa2.0010.0800.0NaN19841069.41322.05.6063.5646021.557348.9779.21NaN73.320.71579
862VUTVanuatu4.0013.0230.022.2230715022.66223.14.3942.6202921.909546.30012.02NaN70.470.60976
110KNASaint Kitts and Nevis15.00281.9970.0NaN53192212.865NaNNaNNaN24654.390NaN12.842.376.230.77974
313SLBSolomon Islands20.0029.1170.025.0068687821.84120.83.5072.0432205.923459.78018.681.473.000.56774
112KNASaint Kitts and Nevis17.00319.5970.0NaN53192212.865NaNNaNNaN24654.390NaN12.842.376.230.77972
650TJKTajikistan13308.001395.3130.031.48953764264.28123.33.4662.1552896.913427.6987.114.871.100.66871
859VUTVanuatu1.003.2560.022.2230715022.66223.14.3942.6202921.909546.30012.02NaN70.470.60969